System and method for super resolution of a sample in a fiber array spectral translator system

ABSTRACT

The disclosure relates generally to methods and apparatus for obtaining a super resolution image of a sample using a fiber array spectral translator system. In one embodiment includes collecting photons from a sample at a first end of a fiber array spectral translator; delivering the photons from a second end of the fiber array spectral translator into a multiple detector rows of a photon detector; interpolating between the multiple detector rows to thereby form interpolated rows; and arranging an output of the multiple detector rows and the interpolated rows so as to obtain a super resolution image of the sample.

PRIORITY INFORMATION

The instant disclosure claims the filing-date benefit of ProvisionalApplication No. 60/772,624 filed 13 Feb. 2006, entitled “ImageReconstruction in a Fiber Array Spectral Translator (FAST) System”, thedisclosure of which is incorporated herein in its entirety.

RELATED APPLICATION

The current application is being filed concurrently with U.S. patentapplication Ser. No. ______ entitled “System and Method for ImageReconstruction in a Fiber Array Spectral Translator System”, thedisclosure of which is incorporated herein in its entirety.

BACKGROUND

A fiber array spectral translator (“FAST”) system when used inconjunction with a photon detector allows massively parallel acquisitionof full-spectral images. A FAST system can provide rapid real-timeanalysis for quick detection, classification, identification, andvisualization of the sample. The FAST technology can acquire a few tothousands of full spectral range, spatially resolved spectrasimultaneously. A typical FAST array contains multiple optical fibersthat may be arranged in a two-dimensional array on one end and a onedimensional (i.e., linear) array on the other end. The linear array isuseful for interfacing with a photon detector, such as a charge-coupleddevice (“CCD”). The two-dimensional array end of the FAST is typicallypositioned to receive photons from a sample. The photons from the samplemay be, for example, emitted by the sample, reflected off of the sample,refracted by the sample, fluoresce from the sample, or scattered by thesample. The scattered photons may be Raman photons.

In a FAST spectrographic system, photons incident to the two-dimensionalend of the FAST may be focused so that a spectroscopic image of thesample is conveyed onto the two-dimensional array of optical fibers. Thetwo-dimensional array of optical fibers may be drawn into aone-dimensional distal array with, for example, serpentine ordering. Theone-dimensional fiber stack may be operatively coupled to an imagingspectrograph of a photon detector, such as a charge-coupled device so asto apply the photons received at the two-dimensional end of the FAST tothe detector rows of the photon detector.

One advantage of this type of apparatus over other spectroscopicapparatus is speed of analysis. A complete spectroscopic imaging dataset can be acquired in the amount of time it takes to generate a singlespectrum from a given material. Additionally, the FAST can beimplemented with multiple detectors. The FAST system allows formassively parallel acquisition of full-spectral images. A FAST fiberbundle may feed optical information from its two-dimensional non-linearimaging end (which can be in any non-linear configuration, e.g.,circular, square, rectangular, etc.) to its one-dimensional lineardistal end input into the photon detector.

A problem exists with the prior art's use of a FAST system. The lineararray end of the FAST, when input into a photon detector, may becomeslightly misaligned so that an image produced may be shifted due to themisalignment. Furthermore, the peaks in a spectrum of the sample may notbe aligned with the peaks of a known calibrated sample of the samesubstance and therefore the received peaks may not be calibrated.Additionally, the fibers in the FAST may not allow for a resolution ofthe resulting image to a degree necessary. The present disclosure, asdescribed herein below, presents methods and systems for overcomingthese deficiencies in the prior art.

The combination of calibration and reconstruction methods according toone embodiment of the present disclosure may be useful among fiberoptics imaging manufacturers. The calibration and image reconstructionapproaches discussed herein are independent of any specific FAST-basedimaging applications. Accordingly, it is an object of the presentdisclosure to provide a method for obtaining a super resolution image ofa sample, comprising: collecting photons from said sample at a first endof a fiber array spectral translator; delivering said photons from asecond end of said fiber array spectral translator into a photondetector, wherein the photons from one fiber in said fiber arrayspectral translator are received by plural detector rows in saiddetector such that said photons received by each detector row have anassociated received photon intensity value; interpolating between saidplural detector rows to thereby form interpolated rows, wherein each ofsaid interpolated rows is associated with an interpolated photonintensity value derived from the received photon intensity values of itsneighboring detector rows; and arranging an output of said pluraldetector rows and said interpolated rows comprising said received photonintensity values and said interpolated photon intensity values,respectively, so as to obtain a super resolution image of said sample.

It is a further object of the present disclosure to provide a system forobtaining a super resolution image of a sample, comprising: a fiberarray spectral translator which collects photons from said sample at afirst end of said fiber array spectral translator; a photon detectoroperatively connected to said fiber array spectral translator forreceiving photons delivered from a second end of said fiber arrayspectral translator, wherein the photons from one fiber in said fiberarray spectral translator are received by plural detector rows in saiddetector such that said photons received by each detector row have anassociated received photon intensity value; a microprocessor unit forinterpolating between said plural detector rows to thereby forminterpolated rows, wherein each of said interpolated rows is associatedwith an interpolated photon intensity value derived from the receivedphoton intensity values of its neighboring detector rows; and saidmicroprocessor unit for arranging an output of said plural detector rowsand said interpolated rows comprising said received photon intensityvalues and said interpolated photon intensity values, respectively, soas to obtain a super resolution image of said sample.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic drawing of a fiber array spectral translator.

FIG. 2 is a is a schematic drawing of a fiber array spectral translatorshowing an exemplary spatial mapping arrangement.

FIG. 3 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 4 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 5 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 6 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 7 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 8 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 9 is a flow chart of a method for spectral calibration according toone embodiment of the disclosure.

FIG. 10 is a block diagram of a system for spectral calibrationaccording to one embodiment of the disclosure.

FIG. 11 is a representation of a raw image and a calibrated image of theoutput of a detector according to an embodiment of the disclosure.

FIG. 12 is a representation of a display output showing an exemplary setof images depicting spectra of two regions of interest in a sample foran uncalibrated condition and a calibrated condition according to anembodiment of the disclosure.

FIG. 13 is a simplified schematic drawing of a FAST system depictingimage reconstruction according to an embodiment of the disclosure.

FIG. 14 is a simplified schematic drawing of a FAST system depictinginterpolation of detector rows for obtaining a super resolution imageaccording to an embodiment of the disclosure.

FIG. 15 is a flow chart of a method for obtaining a super resolutionimage according to an embodiment of the disclosure.

DETAILED DESCRIPTION

The present disclosure relates to spectroscopic systems, particularlyfiber arrays spectral translator (“FAST”) spectroscopic systems, andmore particularly to systems and method for overcoming the alignment andcalibration issues present in the prior art. FAST technology can acquirea few to thousands of full spectral range, spatially resolved spectrasimultaneously. This may be done by focusing a spectroscopic image ontoa two-dimensional array of optical fibers that are drawn into aone-dimensional distal array with, for example, serpentine ordering. Theone-dimensional fiber stack is coupled to an imaging spectrograph. Amicroprocessor and/or software may be used to extract spectral/spatialinformation that is embedded in a single charge-coupled device (“CCD”)image frame.

One of the fundamental advantages of this method over otherspectroscopic methods is speed of analysis. A complete spectroscopicimaging data set can be acquired in the amount of time it takes togenerate a single spectrum from a given material. FAST can beimplemented with multiple detectors. Color-coded FAST spectroscopicimages can be superimposed on other high-spatial resolution gray-scaleimages to provide significant insight into the morphology and chemistryof the sample.

The FAST system allows for massively parallel acquisition offull-spectral images. A FAST fiber bundle may feed optical informationfrom its two-dimensional non-linear imaging end (which can be in anynon-linear configuration, e.g., circular, square, rectangular, etc.) toits one-dimensional linear distal end. The distal end feeds the opticalinformation into associated detector rows. The detector may be a CCDdetector having a fixed number of rows with each row having apredetermined number of pixels. For example, in a 1024-width squaredetector, there will be 1024 pixels (related to, for example, 1024spectral wavelengths) per each of the 1024 rows.

The construction of the FAST array requires knowledge of the position ofeach fiber at both the imaging end and the distal end of the array asshown, for example, in the simplified diagram for FIG. 1 where a totalof sixteen fibers are shown numbered in correspondence between theimaging end 101 and the distal end 102 of the fiber bundle. As shown inFIG. 1, a FAST fiber bundle may feed optical information from its 2Dnon-linear imaging end 101 (which can be in any non-linearconfiguration, e.g., circular, square, rectangular, etc.) to its 2Dlinear distal end 102, which feeds the optical information intoassociated detector rows 103. The distal end may be positioned at theinput to a photon detector 103, such as a CCD, a complementary metaloxide semiconductor (“CMOS”) detector, or a focal plane array sensor(such as InGaAs, InSb, metal oxide semiconductor controlled thyristor(“MCT”), etc.). Photons exiting the distal end fibers may be collectedby the various detector rows. Each fiber collects light from a fixedposition in the two-dimensional array (imaging end) and transmits thislight onto a fixed position on the detector (through that fiber's distalend).

FIG. 2 shows a non-limiting exemplary spatial arrangement of fibers atthe imaging end 201 and the distal end 202. Additionally, as shown inFIG. 2, each fiber may span more than one detector row in detector 203,allowing higher resolution than one pixel per fiber in the reconstructedimage. In fact, this super-resolution, combined with interpolationbetween fiber pixels (i.e., pixels in the detector associated with therespective fiber), achieves much higher spatial resolution than isotherwise possible. Thus, spatial calibration may involve not only theknowledge of fiber geometry (i.e., fiber correspondence) at the imagingend and the distal end, but also the knowledge of which detector rowsare associated with a given fiber.

The calibration of a FAST spectroscopic system may involve spatialcalibration and spectral calibration. The construction of the FAST arrayrequires knowledge of the position of each fiber at both the imaging endand the distal end of the array as shown, for example, in the simplifieddiagram of FIG. 2 where a total of sixteen (16) fibers are shownnumbered in correspondence between the imaging end and the distal end ofthe fiber bundle. Each fiber collects light from a fixed position in the2D array 201 (imaging end) and transmits this light onto a fixedposition on the detector 203 (through that fiber's distal end 202). Itshall be understood by those of skill in the art that the number,arrangement, and numbering of the fibers in FIG. 2 is exemplary only andshould in no way be construed to limit the current disclosure.

As shown in FIG. 2, each fiber may span more than one detector row,allowing higher resolution than one pixel per fiber in the reconstructedimage. In fact, this super-resolution, combined with interpolationbetween fiber pixels (i.e., pixels in the detector associated with therespective fiber), achieves much higher spatial resolution than isotherwise possible. Thus, spatial calibration may involve not only theknowledge of fiber geometry (i.e., fiber correspondence) at the imagingend and the distal end, but also the knowledge of which detector rowsare associated with a given fiber.

The existence of multiple fibers in the FAST requires that each fiber beindependently spectrally calibrated. In practice, a miscalibration maymanifest itself as each fiber producing spectra with bulk spectralshifts (i.e., a spectral shift of one or more pixels) relative to astandard sample such as acetaminophen. Thus, even though a fiber may behardware-wise “linked” or “calibrated” to a group of detector rows (forexample, as part of a spatial calibration exercise), the spectralcalibration may still be performed with a microprocessor and/or insoftware to “align” the optical spectra among the rows associated with aparticular fiber. Such spectral calibration may also help when there arephysical misalignments between a fiber and its associated set ofdetector rows.

FIG. 3 illustrates a broad overview of a spectral calibration methodaccording to one embodiment of the present disclosure. Initially, atblock 301 a detector image may be obtained of a known substance, e.g.,an acetaminophen sample, using a fiber bundle in a FAST system. It isobserved that raw detector data at this stage may look like that shownat the top image in FIG. 11, labeled “Raw Detector Data” 1101. Thejagged or misaligned imagery in the raw detector data display may relateto spectral shifts due to non-calibration. Removing these shifts fromeach fiber (i.e., in fact, each detector row associated with that fiber,because of the super-resolution effect) may be accomplished with amicroprocessor and/or in software using an algorithm, such as acorrelation-based algorithm (e.g., cross-correlation based matching, asis known in the art), as shown in block 302. Comparing the response ofone or more detector rows to that of a calibrated acetaminophenstandard, as part of the cross-correlation based matching, allows theone or more detector row's shift value (in pixels) to be determined, asshown in block 303. For example, one detector row may have a shift valueof “+1” whereas another detector row may have a shift value of “−4”, andso on. As shown in FIG. 3, after computing the shift values, amicroprocessor and/or software may act on the raw detector data toremove the shifts, thereby resulting in spectral shifting of the data inthe one or more detector rows, as indicated in block 304. The spectralshifting may generate an aligned or calibrated output as shown in thebottom image in FIG. 11, labeled “Calibrated” 1102.

As shown in FIG. 3, after the shifts are removed, a peak-matchingalgorithm may be applied at block 306 to set the wavelength range of theraw detector data based on information of known acetaminophen peaks inblock 305. Once this is completed, a calibrated image may result atblock 307. As part of the peak matching algorithm, the acetaminophenstandard spectrum may again be applied to each detector row spectrum(now adjusted through spectral shifting) to obtain a wavelength range.

With reference now directed towards FIG. 4, another exemplary method forcalibrating an image is presented. At block 401 a first image of a knownsubstance is obtained. The first image may typically be obtained byusing a FAST system the linear array end of which is at the input of aphoton detector. The known substance may be any substance for which aknown reference image exists, such as for acetaminophen. At block 402, asecond image of the known substance may be provided. The second imagemay be obtained from an electronic memory device. The first and secondimages each include at least one pixel and preferably multiple pixels.This second image may be a reference, or library, image which will beused to compare the first image against, as shown in block 403. Thiscomparison may be a correlation algorithm, such as across-correlation-based algorithm. At block 404, the first image isadjusted based on the comparison of the first and second images tothereby obtain an adjusted, or calibrated, image. The adjustment maycomprise adjusting at least one row of the first image. As discussedabove, FIG. 11 shows an exemplary image of raw detector data andcalibrated data.

FIG. 5 is a further block diagram where blocks 501 through 504correspond to blocks 401 through 404, respectively, in FIG. 4. Afteradjusting the first image in block 504, a first spectrum may be obtainedof the known substance at block 505. A second spectrum of the knownsubstance may be provided at block 506, where the second spectrum may bea reference, or library, spectrum. Those of skill in the art willreadily understand that the first and second spectrum may be obtained atany time and not necessarily after the adjustment of the first image.The first and second spectra preferably each include at least one peak,more preferably the peak in the first and second spectrum correspond toeach other. At block 507, a comparison of the first and second spectrais performed and based on that comparison, at block 508 an adjustment ismade to the first spectrum.

With attention now drawn to FIG. 6, another spectral calibration methodis depicted. At block 601 a first data set of a known substance isobtained. The first data set may be representative of a first image of aknown substance. The first data set may typically be obtained by using aFAST system the linear array end of which is at the input of a photondetector. A microprocessor may be operatively connected to the photondetector for obtaining the first data set. The known substance may beany substance for which a known reference image exists, such as foracetaminophen. At block 602, a second data set of the known substancemay be provided. The second data set may be representative of a secondimage of a known substance. The second data set may be obtained from anelectronic memory device operatively connected to the microprocessor.The first and second images each include at least one pixel andpreferably multiple pixels. This second data set may be a reference, orlibrary, data set which will be used to compare the first data setagainst, as shown in block 603. This comparison may be a correlationalgorithm, such as a cross-correlation-based algorithm. In block 604,the first data set is adjusted based on the comparison of the first andsecond data sets to thereby obtain an adjusted, or calibrated, image.The adjustment may comprise adjusting at least one row of the first dataset.

FIG. 7 is a further block diagram where blocks 701 through 704correspond to blocks 601 through 604, respectively, in FIG. 6. Afteradjusting the first data set in block 704, a third data set may beobtained of the known substance at block 505. The third data set may berepresentative of a first spectrum of the known substance. A fourth dataset may be provided of the known substance at block 706. The fourth dataset may be representative of a second spectrum of the known substance,where the second spectrum may be a reference, or library, spectrum.Those of skill in the art will readily understand that the third andfourth data sets may be obtained at any time and not necessarily afterthe adjustment of the first image at block 704. The first and secondspectra preferably each include at least one peak, more preferably thepeak in the first and second spectrum correspond to each other. At block707, a comparison of the third and fourth data sets is performed andbased on that comparison, at block 708 an adjustment is made to thethird data set.

FIG. 8 represents yet a further method for spectral calibration. Atblock 801 a first image of a known substance is obtained. The firstimage may typically be obtained by using a FAST system the linear arrayend of which is at the input of a photon detector. The known substancemay be any substance for which a known reference image exists, such asfor acetaminophen. At block 802, a second image of the known substancemay be provided. The second image may be obtained from an electronicmemory device. The first and second images each include at least onepixel and preferably multiple pixels. This second image may be areference, or library, image which will be used to compare the firstimage against. At block 803 a first spectrum of the known substance isobtained, such as by using a FAST system as described herein. At block804 a second spectrum of the known substance may be provided, where thesecond spectrum may be a reference, or library, spectrum. The first andsecond spectra preferably each include at least one peak, morepreferably the peak in the first and second spectrum correspond to eachother. At block 805 a comparison of the first and second images isperformed to thereby obtain a bulk shift adjustment, i.e., an adjustmentof one or more pixels. This comparison may be a correlation algorithm,such as a cross-correlation-based algorithm. At block 806, a comparisonof the first and second spectra is performed to thereby obtain asubpixel adjustment, i.e., an adjustment of less than one pixel.

Referring now to FIG. 9, still a further method for spectral calibrationis presented. At block 901 a first data set of a known substance isobtained, where the first data set may be representative of a firstimage of the known substance. The first data set may typically beobtained by using a FAST system the linear array end of which is at theinput of a photon detector. The known substance may be any substance forwhich a known reference image exists, such as for acetaminophen. Atblock 902, a second data set of the known substance may be provided. Thesecond data set may be representative of a second image of the knownsubstance. The second data set may be obtained from an electronic memorydevice. The first and second data sets each include at least one pixeland preferably multiple pixels. This second data set may be a reference,or library, data set which will be used to compare the first data setagainst. At block 903 a third data set of the known substance isobtained, such as by using a FAST system as described herein. The thirddata set may be representative of a first spectrum of the knownsubstance. At block 904 a fourth data set of the known substance may beprovided, where the fourth data set may be a reference, or library, dataset. The fourth data set may be representative of a second spectrum ofthe known substance. The third and fourth data sets preferably eachinclude at least one peak, more preferably the peak in the third andfourth data sets correspond to each other. At block 905 a comparison ofthe first and second data sets is performed to thereby obtain a bulkshift adjustment, i.e., an adjustment of one or more pixels. Thiscomparison may be a correlation algorithm, such as across-correlation-based algorithm. At block 906, a comparison of thethird and fourth data sets is performed to thereby obtain a subpixeladjustment, i.e., an adjustment of less than one pixel.

FIG. 10 is a block diagram of an exemplary embodiment of a system forperforming a spectral calibration as described herein. Those of skill inthe art will readily understand that the present disclosure is notlimited to the block diagram configuration of FIG. 10. A photon source1001 provides photons for interacting with the sample 1002 so as toprovide photons that enter the two-dimensional array end of the FAST1003. The photons from the sample may be, for example, emitted by thesample, reflected off of the sample, refracted by the sample, fluorescefrom the sample, or scattered by the sample. The scattered photons maybe Raman photons. The photons then travel through the FAST system to thelinear array end at the input of the photon detector 1004. The photondetector 1004 detects the photons and a microprocessor unit 1005,connected to the photon detector 1004, may receive a signal from thephoton detector representative of the detected photons, such as areceived data set. The microprocessor unit may also be connected to amemory unit 1006 which may contain a reference, or library, data set ofthe sample. The microprocessor unit 1005 may obtain from the memory unit1006 the reference data set and compare the reference data set with thereceived data set. This comparison may occur in hardware, software,firmware, or some combination thereof. The microprocessor unit may thenadjust the received data set based on the comparison of the received andreference data sets to thereby form an adjusted data set. Additionally,a display unit 1007 may be connected to the microprocessor. The displayunit 1007 may display an image or images representative of the receiveddata set, the reference data set, the adjusted data set, or combinationsthereof.

As discussed above, FIG. 11 is a representation of a raw image 1101 anda calibrated image 1102 of the output of a detector according to anembodiment of the disclosure. Raw detector data may look like that shownat the top image in FIG. 11, labeled “Raw Detector Data” 1101. Spectralshifting may generate an aligned or calibrated output as shown in thebottom image in FIG. 11, labeled “Calibrated” 1102.

FIG. 12 is a representation of a display output showing an exemplary setof images depicting spectra of two regions of interest in a sample foran uncalibrated condition and a calibrated condition according to anembodiment of the disclosure. For the uncalibrated condition, an imageof a sample 1203 indicates a first region of interest 1 and a secondregion of interest 2. Spectra for these two regions of interest areshown in 1201 where the peaks at 1205 a and 1206 a show misalignment.For the calibrated condition, an image of the sample 1204 indicates thefirst and second regions of interest, 1 and 2, respectively, as above.The spectra for these two regions of interest are shown in 1202 aftercalibration as described herein. Note that the peaks at 1205 b and 1206b are now aligned. FIG. 12, or parts thereof, may be displayed on adisplay unit as discussed above in reference to FIG. 11.

With reference now to FIG. 13, a FAST system is shown with thetwo-dimensional array end 1301 receiving photons from a sample (notshown for clarity) and the linear array end 1302 providing the photonsto the photon detector 1303, where the detector 1303 is shown withmultiple rows. With the detector rows properly calibrated (spectrally)and the mapping back to image positions known (i.e., knowledge ofmapping of each fiber to its associated set of detector rows throughspatial calibration), image reconstruction can be performed according toone embodiment of the present disclosure as discussed herein. Dependingon the number of detector rows that each fiber spans, the pixel block1304 of the reconstructed image is defined. For instance, if each fiberspans 9 detector rows, each fiber may be reconstructed as a 3×3 block ofpixels as shown. Without knowledge of the super-resolution spatialmapping with each fiber, a simple raster filling of the 3×3 block may beused.

The overall dimensions of the image may be calculated. For example, if asquarely-packed 4×4 fiber array (i.e., 16 fibers at the imaging end) asshown spans 9×16 detector rows (i.e., nine rows for each of the 16fibers) on a 1024-width detector (i.e., each detector row having 1024pixels, as shown), the reconstructed hyperspectral image will be(4*3)×(4*3)×1024=12×12×1024, because the imaging end fiber array size is4×4 and there is a 3×3 block of detector pixels associated with eachfiber in the fiber array. The reconstruction is accomplished for eachfiber as follows and as shown in the simplified diagram of FIG. 13.Those of skill in the art will readily understand that the exemplaryfigures used above are in no way to be interpreted as limiting thedisclosure in any way. Assuming that the fibers at the imaging end arein a square configuration as shown in FIG. 13, the data from the ninerelevant detector rows for each fiber (in this exemplary embodiment) isread from the calibrated detector array and arranged in a 3×3×1024block. This block is placed in its corresponding location in the12×12×1024 image 1304. Each fiber may be reconstructed similarly untilthe image is built.

High definition and high resolution images, including super-resolutionimages, can be created by estimating the physical location of fibersrelative to the sample and the separation between fibers, andinterpolating FAST image pixels accordingly.

FIG. 14 shows an exemplary arrangement of detector rows and interpolatedrows according to an embodiment of the disclosure. The linear array end1402 of a FAST system is shown at the input to a photon detector 1403having multiple rows. As shown in relation to fiber 1 in the FASTsystem, multiple detector rows receive the photons from fiber 1. Thesedetector rows are depicted as circles labeled 1403 a through 1403 e inthe layout 1405. Interpolating between the detector rows results ininterpolated rows depicted as squares labeled 1404 a through 1404 d.Therefore, the layout 1405 is composed of detector rows 1403 a through1403 e and interpolated rows 1404 a through 1404 d. These nine rows maybe arranged as shown in FIG. 13 as the nine small boxes comprising block1 of the image reconstruction block 1304. As will be obvious to those ofskill in the art, the present disclosure is not limited to the specificnumber of fibers, detector rows per fiber, or interpolated rows, asshown in the exemplary embodiment of FIG. 14. Additionally, it will beobvious to those of skill in the art that other fibers in the FASTsystem may also deliver photons to other multiple detector rows of thephoton detector 1403.

With reference now directed towards FIG. 15, a method for superresolution is depicted in flow chart form. At block 1501, photons from asample are collected at a first end of a FAST system. At block 1502,photons are delivered from a second end of the FAST system to, forexample, multiple detector rows in a photon detector, such as a CCD.These received photons have an associated photon intensity value. Atblock 1503, an interpolation between detector rows is performed tothereby form interpolated rows. The interpolation may be an average, aweighted average, a mean, or any other similar mathematical operation.The interpolation may be of the photon intensity values of the detectorrows and may be limited to only the nearest neighboring detector rows.At block 1504, the output of the detector rows and the interpolated rowsare arranged so as to form a super resolution image of the sample.

With reference not directed back to FIG. 10, the system shown may alsobe used to obtain a super resolution image of the sample 1002. Photonsfrom the sample 1002 are collected the two-dimensional array end of theFAST system 1003. The photons are delivered from the linear array end ofthe FAST system to, for example, multiple detector rows in the photondetector 1004. These received photons have an associated photonintensity value. The photon detector 1004 detects the photons and amicroprocessor unit 1005, connected to the photon detector 1004, mayreceive a signal from the photon detector representative of the detectedphotons from the detector rows. The microprocessor unit 1005 may thenperform an interpolation, as discussed above, on the detector rows tothereby form interpolated rows. The interpolation may be an average, aweighted average, a mean, or any other similar mathematical operation.The interpolation may be of the photon intensity values of the detectorrows and may be limited to only the nearest neighboring detector rows.The interpolation may be accomplished in hardware, software, firmware,or a combination thereof. The microprocessor unit 1005 may arrange theoutput of the detector rows and the interpolated rows so as to form asuper resolution image of the sample 1002. This super resolution imageof the sample 1002 may be displayed on the display unit 1007.

The above description is not intended and should not be construed to belimited to the examples given but should be granted the full breadth ofprotection afforded by the appended claims and equivalents thereto.Although the disclosure is described using illustrative embodimentsprovided herein, it should be understood that the principles of thedisclosure are not limited thereto and may include modification theretoand permutations thereof.

1. A method for obtaining a super resolution image of a sample,comprising: collecting photons from said sample at a first end of afiber array spectral translator; delivering said photons from a secondend of said fiber array spectral translator into a photon detector,wherein the photons from one fiber in said fiber array spectraltranslator are received by plural detector rows in said detector suchthat said photons received by each detector row have an associatedreceived photon intensity value; interpolating between said pluraldetector rows to thereby form interpolated rows, wherein each of saidinterpolated rows is associated with an interpolated photon intensityvalue derived from the received photon intensity values of itsneighboring detector rows; and arranging an output of said pluraldetector rows and said interpolated rows comprising said received photonintensity values and said interpolated photon intensity values,respectively, so as to obtain a super resolution image of said sample.2. The method of claim 1 further comprising displaying said arrangedoutput.
 3. The method of claim 1 wherein said plural detector rows arefive in number.
 4. The method of claim 3 wherein said interpolated rowsare four in number.
 5. The method of claim 1 wherein said pluraldetector rows are odd in number and said interpolated rows are even innumber.
 6. The method of claim 1 wherein said first end of said fiberarray spectral translator is arranged substantially in the shape of asquare.
 7. The method of claim 1 wherein said first end of said fiberarray spectral translator is arranged substantially in the shape of acircle.
 8. The method of claim 1 wherein said photon detector isselected from the group consisting of: charge-coupled device (“CCD”),complementary metal oxide semiconductor (“CMOS”) detector, and focalplane array sensor.
 9. The method of claim 1 wherein said interpolatedphoton intensity value for each of said interpolated rows is derivedfrom the received photon intensity values of the detector rowsimmediately neighboring said interpolated row.
 10. A system forobtaining a super resolution image of a sample, comprising: a fiberarray spectral translator which collects photons from said sample at afirst end of said fiber array spectral translator; a photon detectoroperatively connected to said fiber array spectral translator forreceiving photons delivered from a second end of said fiber arrayspectral translator, wherein the photons from one fiber in said fiberarray spectral translator are received by plural detector rows in saiddetector such that said photons received by each detector row have anassociated received photon intensity value; a microprocessor unit forinterpolating between said plural detector rows to thereby forminterpolated rows, wherein each of said interpolated rows is associatedwith an interpolated photon intensity value derived from the receivedphoton intensity values of its neighboring detector rows; and saidmicroprocessor unit for arranging an output of said plural detector rowsand said interpolated rows comprising said received photon intensityvalues and said interpolated photon intensity values, respectively, soas to obtain a super resolution image of said sample.
 11. The system ofclaim 10 further comprising a display device for displaying saidarranged output.
 12. The system of claim 10 wherein said plural detectorrows are five in number.
 13. The system of claim 12 wherein saidinterpolated rows are four in number.
 14. The system of claim 10 whereinsaid plural detector rows are odd in number and said interpolated rowsare even in number.
 15. The system of claim 10 wherein said first end ofsaid fiber array spectral translator is arranged substantially in theshape of a square.
 16. The system of claim 10 wherein said first end ofsaid fiber array spectral translator is arranged substantially in theshape of a circle.
 17. The system of claim 10 wherein said photondetector is selected from the group consisting of: charge-coupled device(“CCD”), complementary metal oxide semiconductor (“CMOS”) detector, andfocal plane array sensor.
 18. The system of claim 10 wherein saidinterpolated photon intensity value for each of said interpolated rowsis derived from the received photon intensity values of the detectorrows immediately neighboring said interpolated row.